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cs.LG2026
Drifting Models for Surrogate Flow Modeling
Chris R. Jung, Markus Dörr, Natalie Jüngling +3
While Computational Fluid Dynamics (CFD) provides high-fidelity flow fields for optimizing indoor environments, its computational cost limits rapid exploration. To solve this probl…
cs.LG2026
Reducing Experimental Testing in Space Propulsion Film Cooling Analyses by Pixelwise Generative Image Interpolation
Adam T. Müller, Philipp J. Teuffel, Konstantin Manassis +1
We propose a machine learning approach for image regression from sparse experimental measurements. We show the application of the proposed method on film cooling studies in propuls…
cs.LG2026
Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learning
Adam T. Müller, Tobias Rögelein, Nicolaj C. Stache
The deployment of deep neural networks in safety-critical systems necessitates reliable and efficient uncertainty quantification (UQ). A practical and widespread strategy for UQ is…